Professional Interests

My professional interests have taken me from planetary
science to internal gravity
waves and their generation to radio occultation to climate monitoring and
finally to Bayesian inference. I remain interested in all of these topics. Most
recently, I have focused on the information content in climate benchmark data
types.

Climate benchmark data types have the unique property that
they can be used to infer climate change with nearly absolute certainty by
comparing to future climate benchmarks. Climate benchmarks are established by
empirical determination of observational uncertainty while the observations are
being made and entails traceability to international standards of units. Not
many data types can be turned into climate benchmarks. Climate benchmarking
marks a change in the climate monitoring paradigm from one dependent on climate
data records, which is based on the assumption of stability of
calibration, to one based in SI traceability.

“What is a climate benchmark?”, you ask. It is a measurement
made in space with a chain of calibration back to the standard that defines its
unit of measure. And you must fully account for all uncertainties along the way,
preferably empirically. I have experience with GPS radio occultation. In
a radio occultation, you can measure the Doppler shift of GPS
signals as they propagate through the Earth’s atmosphere. The only reason you
get Doppler shifts in the signal, other than ordinary vacuum propagation, is
that the atmosphere bends the GPS signals downward. The measurement is one of
timing, the units of which are inverse seconds, or Hertz, so the measurement
must be calibrated against the international
definition of the second by a chain of comparisons. This is done using
atomic clocks, which are traceable to the international definition of the
second with accuracy easily sufficient for climate monitoring. Of course, Doppler
shifts are not terribly exciting, but we can easily invert them to get vertical
profiles of the atmosphere’s index of refraction. The index of refraction is,
for the most part, determined by atmospheric density. You can do a lot with
profiles of atmospheric density, including measuring thermal expansion of the
atmosphere. Another great example of a climate benchmark is the Earth’s emitted
thermal infrared spectrum. My colleague John Dykema focuses on it
among other things. Here is a
more competent description of climate benchmarking.

But what can be learned from measured climate change in
these climate benchmark data types? Most of the research community interested
in multi-decadal climate change would like to know the equilibrium sensitivity
of climate, or how much surface air temperature would increase with a doubling
of carbon dioxide. (They would also like to know how rapidly oceans soak up
atmospheric heat.) Some are satisfied with determinations of equilibrium
sensitivity derived from paleoclimatic data. I’m not, though, because it is
really difficult to prove the accuracy of paleoclimatic records. Thus, I’d
rather see more credible determinations made with modern instruments as soon as
possible. Theoretically, this can be done with climate benchmark data types in
space, particularly with thermal infrared spectra and a shortwave measurement.
This is the foundation of the Climate Absolute Radiance and Refractivity
Observatory (CLARREO), a
multi-spacecraft NASA mission slated for launch in 2017 and 2020.

The methods used to interpret climate benchmark data types
are very much the same as those used in climate signal detection and
attribution, which is what researchers do to figure out who and what is
responsible for climate change. Eventually, methods of data analysis must be
directly linked to climate prediction. Bayesian
inference, the ultimate statistical implementation of the scientific
method, should provide the answer, and optimally so. I expect the
frontiers of climate change analysis to be based in Bayesian inference, so
that’s what I’m working on in close collaboration with Yi Huang.

The Inner Postmodernist

Very often people stop me in the streets, interrupt my
haircuts, or sidetrack me from washing pots in the kitchen with a question
like, “So...do you believe in global warming?” My knee-jerk reaction is to
respond, “It shouldn't be a matter of belief,” but mostly I'm more patient than that. In considering a response, I feel it is a mistake for a scientist to even remotely come off as an environmental advocate when discussing his/her work as it can only lead to loss of credibility of our profession in the long run. Instead, I simply steer my inquisitor toward specifics. Climate change isn't just one question. To the public, it is really three questions:

Is climate changing?

Are humans responsible?

Can we predict climate change?

Well, the answers are “yes,” “highly likely,” and “rather poorly.” My work dwells on the last question. With appreciation for the integrity of my inquisitor, I pose my answers in this framework and find that he/she inevitably responds positively.

The question of belief doesn't really go away, though. I highly
recommend the book Scientific
Method in Practice by Hugh Gauch
on the topic of belief and objectivity in science. Here is one way it arises in
my business. Once one appreciates the seriousness of what’s happening out
there, there is a strong and understandable feeling that something must be
done. How does one determine what must be done? Different people and societies
place different values on the preservation of species, sea level rise, the
disappearance of the winter Olympics, etc. Many are rightly concerned about the
spread of infectious disease, desertification, etc. Then again, some are more
interested in human survival beyond age 30 than in the survival of harlequin
frogs. Where are your values? What do you believe? The response is not strictly
an objective one, but it is still important. Science doesn’t do everything for
us.